COVID-19 EDA
Julia Layne
3/13/2020
Read in Files
covid_raw = read.csv("04-18-2020.csv",header = TRUE)
#covid_ts_confirmed = read.csv(file.choose(),header = TRUE)Look at Raw Data
covid_raw## Province_State Country_Region Last_Update
## 1 Alabama US 2020-04-18 22:32:47
## 2 Alaska US 2020-04-18 22:32:47
## 3 American Samoa US
## 4 Arizona US 2020-04-18 22:32:47
## 5 Arkansas US 2020-04-18 22:32:47
## 6 California US 2020-04-18 22:32:47
## 7 Colorado US 2020-04-18 22:32:47
## 8 Connecticut US 2020-04-18 22:32:47
## 9 Delaware US 2020-04-18 22:32:47
## 10 Diamond Princess US 2020-04-18 22:32:47
## 11 District of Columbia US 2020-04-18 22:32:47
## 12 Florida US 2020-04-18 22:32:47
## 13 Georgia US 2020-04-18 22:32:47
## 14 Grand Princess US 2020-04-18 22:32:47
## 15 Guam US 2020-04-18 22:32:47
## 16 Hawaii US 2020-04-18 22:32:47
## 17 Idaho US 2020-04-18 22:32:47
## 18 Illinois US 2020-04-18 22:32:47
## 19 Indiana US 2020-04-18 22:32:47
## 20 Iowa US 2020-04-18 22:32:47
## 21 Kansas US 2020-04-18 22:32:47
## 22 Kentucky US 2020-04-18 22:32:47
## 23 Louisiana US 2020-04-18 22:32:47
## 24 Maine US 2020-04-18 22:32:47
## 25 Maryland US 2020-04-18 22:32:47
## 26 Massachusetts US 2020-04-18 22:32:47
## 27 Michigan US 2020-04-18 22:32:47
## 28 Minnesota US 2020-04-18 22:32:47
## 29 Mississippi US 2020-04-18 22:32:47
## 30 Missouri US 2020-04-18 22:32:47
## 31 Montana US 2020-04-18 22:32:47
## 32 Nebraska US 2020-04-18 22:32:47
## 33 Nevada US 2020-04-18 22:32:47
## 34 New Hampshire US 2020-04-18 22:32:47
## 35 New Jersey US 2020-04-18 22:32:47
## 36 New Mexico US 2020-04-18 22:32:47
## 37 New York US 2020-04-18 22:32:47
## 38 North Carolina US 2020-04-18 22:32:47
## 39 North Dakota US 2020-04-18 22:32:47
## 40 Northern Mariana Islands US 2020-04-18 22:32:47
## 41 Ohio US 2020-04-18 22:32:47
## 42 Oklahoma US 2020-04-18 22:32:47
## 43 Oregon US 2020-04-18 22:32:47
## 44 Pennsylvania US 2020-04-18 22:32:47
## 45 Puerto Rico US 2020-04-18 22:32:47
## 46 Rhode Island US 2020-04-18 22:32:47
## 47 South Carolina US 2020-04-18 22:32:47
## 48 South Dakota US 2020-04-18 22:32:47
## 49 Tennessee US 2020-04-18 22:32:47
## 50 Texas US 2020-04-18 22:32:47
## 51 Utah US 2020-04-18 22:32:47
## 52 Vermont US 2020-04-18 22:32:47
## 53 Virgin Islands US 2020-04-18 22:32:47
## 54 Virginia US 2020-04-18 22:32:47
## 55 Washington US 2020-04-18 22:32:47
## 56 West Virginia US 2020-04-18 22:32:47
## 57 Wisconsin US 2020-04-18 22:32:47
## 58 Wyoming US 2020-04-18 22:32:47
## 59 Alberta Canada
## 60 Anguilla United Kingdom
## 61 Anhui China
## 62 Aruba Netherlands
## 63 Australian Capital Territory Australia
## 64 Beijing China
## 65 Bermuda United Kingdom
## 66 Bonaire, Sint Eustatius and Saba Netherlands
## 67 British Columbia Canada
## 68 British Virgin Islands United Kingdom
## 69 Cayman Islands United Kingdom
## 70 Channel Islands United Kingdom
## 71 Chongqing China
## 72 Curacao Netherlands
## 73 Falkland Islands (Malvinas) United Kingdom
## 74 Faroe Islands Denmark
## 75 French Guiana France
## 76 French Polynesia France
## 77 Fujian China
## 78 Gansu China
## 79 Gibraltar United Kingdom
## 80 Greenland Denmark
## 81 Guadeloupe France
## 82 Guangdong China
## 83 Guangxi China
## 84 Guizhou China
## 85 Hainan China
## 86 Hebei China
## 87 Heilongjiang China
## 88 Henan China
## 89 Hong Kong China
## 90 Hubei China
## 91 Hunan China
## 92 Inner Mongolia China
## 93 Isle of Man United Kingdom
## 94 Jiangsu China
## 95 Jiangxi China
## 96 Jilin China
## 97 Liaoning China
## 98 Macau China
## 99 Manitoba Canada
## 100 Martinique France
## 101 Mayotte France
## 102 Montserrat United Kingdom
## 103 New Brunswick Canada
## 104 New Caledonia France
## 105 New South Wales Australia
## 106 Newfoundland and Labrador Canada
## 107 Ningxia China
## 108 Northern Territory Australia
## 109 Northwest Territories Canada
## 110 Nova Scotia Canada
## 111 Ontario Canada
## 112 Prince Edward Island Canada
## 113 Qinghai China
## 114 Quebec Canada
## 115 Queensland Australia
## 116 Recovered Canada
## 117 Recovered US
## 118 Reunion France
## 119 Saint Barthelemy France
## 120 Saint Pierre and Miquelon France
## 121 Saskatchewan Canada
## 122 Shaanxi China
## 123 Shandong China
## 124 Shanghai China
## 125 Shanxi China
## 126 Sichuan China
## 127 Sint Maarten Netherlands
## 128 South Australia Australia
## 129 St Martin France
## 130 Tasmania Australia
## 131 Tianjin China
## 132 Tibet China
## 133 Turks and Caicos Islands United Kingdom
## 134 Victoria Australia
## 135 Western Australia Australia
## 136 Xinjiang China
## 137 Yukon Canada
## 138 Yunnan China
## 139 Zhejiang China
## 140 Grand Princess Canada
## Lat Long_ Confirmed Deaths Recovered Active FIPS Incident_Rate
## 1 32.31820 -86.90230 4712 153 NA 4559 1 1.004927e+02
## 2 61.37070 -152.40440 314 9 147 305 2 5.253041e+01
## 3 -14.27100 -170.13200 0 0 NA NA 60 0.000000e+00
## 4 33.72980 -111.43120 4724 180 539 4544 4 6.490155e+01
## 5 34.96970 -92.37310 1744 38 703 1706 5 6.736121e+01
## 6 36.11620 -119.68160 30491 1140 NA 29351 6 7.776606e+01
## 7 39.05980 -105.31110 9047 389 NA 8658 8 1.596488e+02
## 8 41.59780 -72.75540 17550 1086 NA 16464 9 4.922465e+02
## 9 39.31850 -75.50710 2538 67 423 2471 10 2.606381e+02
## 10 NA NA 49 0 0 49 88888 NA
## 11 38.89740 -77.02680 2666 91 608 2575 11 3.777547e+02
## 12 27.76630 -81.68680 25492 748 NA 24744 12 1.200606e+02
## 13 33.04060 -83.64310 17669 673 NA 16996 13 1.742609e+02
## 14 NA NA 103 0 0 103 99999 NA
## 15 13.44430 144.79370 136 5 110 131 66 8.281120e+01
## 16 21.09430 -157.49830 574 9 390 565 15 4.054285e+01
## 17 44.24050 -114.47880 1655 43 453 1612 16 1.027633e+02
## 18 40.34950 -88.98610 29160 1259 NA 27901 17 2.484736e+02
## 19 39.84940 -86.25830 10641 545 NA 10096 18 1.626071e+02
## 20 42.01150 -93.21050 2513 74 1095 2439 19 9.588994e+01
## 21 38.52660 -96.72650 1821 85 NA 1736 20 7.467742e+01
## 22 37.66810 -84.67010 2707 144 979 2563 21 7.907568e+01
## 23 31.16950 -91.86780 23580 1267 NA 22313 22 5.129135e+02
## 24 44.69390 -69.38190 847 32 382 815 23 7.214614e+01
## 25 39.06390 -76.80210 12326 421 771 11905 24 2.073920e+02
## 26 42.23020 -71.53010 36372 1404 NA 34968 25 5.299127e+02
## 27 43.32660 -84.53610 30791 2308 3237 28483 26 3.864692e+02
## 28 45.69450 -93.90020 2209 121 1118 2088 27 4.465833e+01
## 29 32.74160 -89.67870 3974 152 NA 3822 28 1.372614e+02
## 30 38.45610 -92.28840 5579 197 NA 5382 29 9.521362e+01
## 31 46.92190 -110.45440 426 10 234 416 30 4.919242e+01
## 32 41.12540 -98.26810 1249 24 NA 1225 31 8.188703e+01
## 33 38.31350 -117.05540 3626 151 NA 3475 32 1.201977e+02
## 34 43.45250 -71.56390 1342 38 468 1304 33 1.010430e+02
## 35 40.29890 -74.52100 81420 4070 NA 77350 34 9.166658e+02
## 36 34.84050 -106.24850 1798 53 382 1745 35 1.078240e+02
## 37 42.16570 -74.94810 241712 17671 23887 224041 36 1.433555e+03
## 38 35.63010 -79.80640 6328 187 NA 6141 37 6.380399e+01
## 39 47.52890 -99.78400 528 9 183 519 38 8.706866e+01
## 40 15.09790 145.67390 14 2 9 12 69 2.538807e+01
## 41 40.38880 -82.76490 10222 451 NA 9771 39 9.148137e+01
## 42 35.56530 -96.92890 2465 136 1515 2329 40 6.739329e+01
## 43 44.57200 -122.07090 1844 72 NA 1772 41 4.601862e+01
## 44 40.59080 -77.20980 31652 1042 NA 30610 42 2.514559e+02
## 45 18.22080 -66.59010 1118 60 NA 1058 72 3.811267e+01
## 46 41.68090 -71.51180 4491 137 217 4354 44 4.239348e+02
## 47 33.85690 -80.94500 4248 119 2633 4129 45 8.428613e+01
## 48 44.29980 -99.43880 1542 7 552 1535 46 2.095416e+02
## 49 35.74780 -86.69230 6589 142 3234 6447 47 1.003667e+02
## 50 31.05450 -97.56350 18704 476 4806 18228 48 8.137636e+01
## 51 40.15000 -111.86240 2917 25 565 2892 49 1.017837e+02
## 52 44.04590 -72.71070 803 38 15 765 50 1.315119e+02
## 53 18.33580 -64.89630 53 3 46 50 78 4.940896e+01
## 54 37.76930 -78.17000 8053 258 1228 7795 51 1.018435e+02
## 55 47.40090 -121.49050 11776 613 NA 11163 53 1.559858e+02
## 56 38.49120 -80.95450 785 16 255 769 54 5.933493e+01
## 57 44.26850 -89.61650 4199 212 NA 3987 55 8.114682e+01
## 58 42.75600 -107.30250 309 2 206 307 56 6.214303e+01
## 59 53.93330 -116.57650 2562 51 0 2511 NA 5.805382e+01
## 60 18.22060 -63.06860 3 0 1 2 NA 1.999733e+01
## 61 31.82570 117.22640 991 6 984 1 NA 1.567046e+00
## 62 12.52110 -69.96830 96 2 44 50 NA 8.991627e+01
## 63 -35.47350 149.01240 103 3 88 12 NA 2.405980e+01
## 64 40.18240 116.41420 593 8 509 76 NA 2.753018e+00
## 65 32.30780 -64.75050 83 5 35 43 NA 1.332841e+02
## 66 12.17840 -68.23850 3 0 0 3 NA 1.144121e+01
## 67 53.72670 -127.64760 1618 78 0 1540 NA 3.165772e+01
## 68 18.42070 -64.64000 4 0 2 2 NA 1.322883e+01
## 69 19.31330 -81.25460 61 1 7 53 NA 9.281802e+01
## 70 49.37230 -2.36440 484 21 73 390 NA 2.838726e+02
## 71 30.05720 107.87400 579 6 570 3 NA 1.866538e+00
## 72 12.16960 -68.99000 14 1 11 2 NA 8.531383e+00
## 73 -51.79630 -59.52360 11 0 3 8 NA 3.158197e+02
## 74 61.89260 -6.91180 184 0 173 11 NA 3.765476e+02
## 75 4.00000 -53.00000 96 0 64 32 NA 3.214121e+01
## 76 -17.67970 -149.40680 55 0 2 53 NA 1.957964e+01
## 77 26.07890 117.98740 355 1 336 18 NA 9.007866e-01
## 78 37.80990 101.05830 139 2 137 0 NA 5.271141e-01
## 79 36.14080 -5.35360 132 0 111 21 NA 3.917960e+02
## 80 71.70690 -42.60430 11 0 11 0 NA 1.937575e+01
## 81 16.26500 -61.55100 148 8 73 67 NA 3.698826e+01
## 82 23.34170 113.42440 1579 8 1482 89 NA 1.391680e+00
## 83 23.82980 108.78810 254 2 252 0 NA 5.156313e-01
## 84 26.81540 106.87480 146 2 144 0 NA 4.055556e-01
## 85 19.19590 109.74530 168 6 162 0 NA 1.798715e+00
## 86 39.54900 116.13060 328 6 316 6 NA 4.340921e-01
## 87 47.86200 127.76150 892 13 472 407 NA 2.364166e+00
## 88 33.88200 113.61400 1276 22 1254 0 NA 1.328475e+00
## 89 22.30000 114.20000 1024 4 568 452 NA 1.365882e+01
## 90 30.97560 112.27070 68128 4512 63494 122 NA 1.151394e+02
## 91 27.61040 111.70880 1019 4 1015 0 NA 1.477026e+00
## 92 44.09350 113.94480 193 1 104 88 NA 7.616417e-01
## 93 54.23610 -4.54810 297 6 180 111 NA 3.492803e+02
## 94 32.97110 119.45500 653 0 643 10 NA 8.110794e-01
## 95 27.61400 115.72210 937 1 936 0 NA 2.015921e+00
## 96 43.66610 126.19230 102 1 97 4 NA 3.772189e-01
## 97 41.29560 122.60850 146 2 142 2 NA 3.349392e-01
## 98 22.16670 113.55000 45 0 17 28 NA 6.930092e+00
## 99 53.76090 -98.81390 253 5 0 248 NA 1.836638e+01
## 100 14.64150 -61.02420 158 8 73 77 NA 4.210358e+01
## 101 -12.82750 45.16624 254 4 117 133 NA 9.310407e+01
## 102 16.74250 -62.18737 11 0 2 9 NA 2.200440e+02
## 103 46.56530 -66.46190 117 0 0 117 NA 1.500013e+01
## 104 -20.90431 165.61804 18 0 14 4 NA 6.304927e+00
## 105 -33.86880 151.20930 2926 26 1379 1521 NA 3.604336e+01
## 106 53.13550 -57.66040 257 3 0 254 NA 4.929368e+01
## 107 37.26920 106.16550 75 0 75 0 NA 1.090116e+00
## 108 -12.46340 130.84560 28 0 9 19 NA 1.140065e+01
## 109 64.82550 -124.84570 5 0 0 5 NA 1.113487e+01
## 110 44.68200 -63.74430 649 7 0 642 NA 6.639678e+01
## 111 51.25380 -85.32320 11013 564 0 10449 NA 7.485814e+01
## 112 46.51070 -63.41680 26 0 0 26 NA 1.643926e+01
## 113 35.74520 95.99560 18 0 18 0 NA 2.985075e-01
## 114 52.93990 -73.54910 17521 688 0 16833 NA 2.052198e+02
## 115 -27.46980 153.02510 1015 6 738 271 NA 1.984166e+01
## 116 NA NA 0 0 10964 -10964 NA NA
## 117 NA NA 0 0 64840 -64840 NA NA
## 118 -21.11510 55.53640 407 0 237 170 NA 4.545922e+01
## 119 17.90000 -62.83330 6 0 5 1 NA 6.069803e+01
## 120 46.88520 -56.31590 1 0 0 1 NA 1.725626e+01
## 121 52.93990 -106.45090 313 4 0 309 NA 2.648803e+01
## 122 35.19170 108.87010 256 3 252 1 NA 6.625259e-01
## 123 36.34270 118.14980 787 7 765 15 NA 7.833184e-01
## 124 31.20200 121.44910 628 7 512 109 NA 2.590759e+00
## 125 37.57770 112.29220 197 0 135 62 NA 5.298548e-01
## 126 30.61710 102.71030 561 3 553 5 NA 6.725812e-01
## 127 18.04250 -63.05480 64 9 12 43 NA 1.492468e+02
## 128 -34.92850 138.60070 435 4 331 100 NA 2.476516e+01
## 129 18.07080 -63.05010 37 2 19 16 NA 9.570863e+01
## 130 -42.88210 147.32720 180 7 67 106 NA 3.361345e+01
## 131 39.30540 117.32300 189 3 173 13 NA 1.211538e+00
## 132 31.69270 88.09240 1 0 1 0 NA 2.906977e-02
## 133 21.69400 -71.79790 11 1 0 10 NA 2.841056e+01
## 134 -37.81360 144.96310 1319 14 1172 133 NA 1.989472e+01
## 135 -31.95050 115.86050 541 7 340 194 NA 2.056565e+01
## 136 41.11290 85.24010 76 3 73 0 NA 3.055891e-01
## 137 64.28230 -135.00000 9 0 0 9 NA 2.190954e+01
## 138 24.97400 101.48700 184 2 177 5 NA 3.809524e-01
## 139 29.18320 120.09340 1268 1 1246 21 NA 2.210214e+00
## 140 NA NA 13 0 0 13 NA NA
## People_Tested People_Hospitalized Mortality_Rate UID ISO3 Testing_Rate
## 1 42538 620 3.24702886 84000001 USA 907.206961
## 2 9655 39 2.86624204 84000002 USA 1615.226458
## 3 3 NA NA 16 ASM 5.391708
## 4 51045 566 3.81033023 84000004 USA 701.291175
## 5 24141 291 2.17889908 84000005 USA 932.435235
## 6 251614 4892 3.73880817 84000006 USA 641.731334
## 7 43307 1755 4.29976788 84000008 USA 764.221442
## 8 55462 1946 6.18803419 84000009 USA 1555.611091
## 9 14017 224 2.63987392 84000010 USA 1439.465825
## 10 NA NA 0.00000000 84088888 USA NA
## 11 13268 313 3.41335334 84000011 USA 1879.988494
## 12 246527 3842 2.93425388 84000012 USA 1161.077449
## 13 74208 3420 3.80893090 84000013 USA 731.878131
## 14 NA NA 0.00000000 84099999 USA NA
## 15 1079 6 3.67647059 316 GUM 657.009420
## 16 22343 48 1.56794425 84000015 USA 1578.133984
## 17 16609 151 2.59818731 84000016 USA 1031.296550
## 18 137404 4340 4.31755830 84000017 USA 1170.825347
## 19 56873 NA 5.12169909 84000018 USA 869.086983
## 20 22947 193 2.94468762 84000019 USA 875.601411
## 21 17676 383 4.66776496 84000020 USA 724.875415
## 22 30596 1008 5.31954193 84000021 USA 893.756702
## 23 137999 1761 5.37319763 84000022 USA 3001.762352
## 24 14923 136 3.77804014 84000023 USA 1271.117865
## 25 65370 2757 3.41554438 84000024 USA 1099.887521
## 26 156806 3729 3.86011217 84000025 USA 2284.545582
## 27 99727 3634 7.49569679 84000026 USA 1251.710281
## 28 44268 561 5.47759167 84000027 USA 894.945583
## 29 38765 782 3.82486160 84000028 USA 1338.937540
## 30 53759 875 3.53109876 84000029 USA 917.474250
## 31 10569 55 2.34741784 84000030 USA 1220.457120
## 32 13677 NA 1.92153723 84000031 USA 896.692524
## 33 30751 NA 4.16436845 84000032 USA 1019.359916
## 34 12852 190 2.83159463 84000033 USA 967.663242
## 35 162536 7718 4.99877180 84000034 USA 1829.909065
## 36 35613 242 2.94771969 84000035 USA 2135.669879
## 37 596532 54339 7.31076653 84000036 USA 3537.934378
## 38 76211 388 2.95512010 84000037 USA 768.420655
## 39 12963 47 1.70454545 84000038 USA 2137.634437
## 40 56 NA 14.28571429 580 MNP 101.552299
## 41 78758 2519 4.41205244 84000039 USA 704.841478
## 42 35536 541 5.51724138 84000040 USA 971.557023
## 43 37583 449 3.90455531 84000041 USA 937.916295
## 44 153965 2613 3.29205106 84000042 USA 1223.158186
## 45 9173 NA 5.36672630 630 PRI 312.707949
## 46 32826 331 3.05054554 84000044 USA 3098.660419
## 47 38833 776 2.80131827 84000045 USA 770.499854
## 48 11660 68 0.45395590 84000046 USA 1584.471634
## 49 90586 719 2.15510700 84000047 USA 1379.847368
## 50 176239 1321 2.54491018 84000048 USA 766.771225
## 51 59944 251 0.85704491 84000049 USA 2091.642294
## 52 12566 56 4.73225405 84000050 USA 2058.006096
## 53 443 NA 5.66037736 850 VIR 412.984301
## 54 51931 1307 3.20377499 84000051 USA 656.753376
## 55 131627 518 5.20550272 84000053 USA 1743.541840
## 56 19440 83 2.03821656 84000054 USA 1469.389976
## 57 48161 1176 5.04882115 84000055 USA 930.724499
## 58 6522 50 0.64724919 84000056 USA 1311.640254
## 59 NA NA 1.99063232 12401 CAN NA
## 60 NA NA 0.00000000 660 AIA NA
## 61 NA NA 0.60544904 15601 CHN NA
## 62 NA NA 2.08333333 533 ABW NA
## 63 NA NA 2.91262136 3601 AUS NA
## 64 NA NA 1.34907251 15602 CHN NA
## 65 NA NA 6.02409639 60 BMU NA
## 66 NA NA 0.00000000 535 BES NA
## 67 NA NA 4.82076638 12402 CAN NA
## 68 NA NA 0.00000000 92 VGB NA
## 69 NA NA 1.63934426 136 CYM NA
## 70 NA NA 4.33884298 8261 GBR NA
## 71 NA NA 1.03626943 15603 CHN NA
## 72 NA NA 7.14285714 531 CUW NA
## 73 NA NA 0.00000000 238 FLK NA
## 74 NA NA 0.00000000 234 FRO NA
## 75 NA NA 0.00000000 254 GUF NA
## 76 NA NA 0.00000000 258 PYF NA
## 77 NA NA 0.28169014 15604 CHN NA
## 78 NA NA 1.43884892 15605 CHN NA
## 79 NA NA 0.00000000 292 GIB NA
## 80 NA NA 0.00000000 304 GRL NA
## 81 NA NA 5.40540541 312 GLP NA
## 82 NA NA 0.50664978 15606 CHN NA
## 83 NA NA 0.78740157 15607 CHN NA
## 84 NA NA 1.36986301 15608 CHN NA
## 85 NA NA 3.57142857 15609 CHN NA
## 86 NA NA 1.82926829 15610 CHN NA
## 87 NA NA 1.45739910 15611 CHN NA
## 88 NA NA 1.72413793 15612 CHN NA
## 89 NA NA 0.39062500 344 HKG NA
## 90 NA NA 6.62282762 15613 CHN NA
## 91 NA NA 0.39254171 15614 CHN NA
## 92 NA NA 0.51813472 15615 CHN NA
## 93 NA NA 2.02020202 833 IMN NA
## 94 NA NA 0.00000000 15616 CHN NA
## 95 NA NA 0.10672359 15617 CHN NA
## 96 NA NA 0.98039216 15618 CHN NA
## 97 NA NA 1.36986301 15619 CHN NA
## 98 NA NA 0.00000000 446 MAC NA
## 99 NA NA 1.97628458 12403 CAN NA
## 100 NA NA 5.06329114 474 MTQ NA
## 101 NA NA 1.57480315 175 MYT NA
## 102 NA NA 0.00000000 500 MSR NA
## 103 NA NA 0.00000000 12404 CAN NA
## 104 NA NA 0.00000000 540 NCL NA
## 105 NA NA 0.88858510 3602 AUS NA
## 106 NA NA 1.16731518 12405 CAN NA
## 107 NA NA 0.00000000 15620 CHN NA
## 108 NA NA 0.00000000 3603 AUS NA
## 109 NA NA 0.00000000 12406 CAN NA
## 110 NA NA 1.07858243 12407 CAN NA
## 111 NA NA 5.12122038 12408 CAN NA
## 112 NA NA 0.00000000 12409 CAN NA
## 113 NA NA 0.00000000 15621 CHN NA
## 114 NA NA 3.92671651 12410 CAN NA
## 115 NA NA 0.59113300 3604 AUS NA
## 116 NA NA NA 12415 CAN NA
## 117 NA NA NA 84070001 USA NA
## 118 NA NA 0.00000000 638 REU NA
## 119 NA NA 0.00000000 652 BLM NA
## 120 NA NA 0.00000000 666 SPM NA
## 121 NA NA 1.27795527 12411 CAN NA
## 122 NA NA 1.17187500 15622 CHN NA
## 123 NA NA 0.88945362 15623 CHN NA
## 124 NA NA 1.11464968 15624 CHN NA
## 125 NA NA 0.00000000 15625 CHN NA
## 126 NA NA 0.53475936 15626 CHN NA
## 127 NA NA 14.06250000 534 SXM NA
## 128 NA NA 0.91954023 3605 AUS NA
## 129 NA NA 5.40540541 663 MAF NA
## 130 NA NA 3.88888889 3606 AUS NA
## 131 NA NA 1.58730159 15627 CHN NA
## 132 NA NA 0.00000000 15628 CHN NA
## 133 NA NA 9.09090909 796 TCA NA
## 134 NA NA 1.06141016 3607 AUS NA
## 135 NA NA 1.29390018 3608 AUS NA
## 136 NA NA 3.94736842 15629 CHN NA
## 137 NA NA 0.00000000 12412 CAN NA
## 138 NA NA 1.08695652 15630 CHN NA
## 139 NA NA 0.07886435 15631 CHN NA
## 140 NA NA 0.00000000 12414 CAN NA
## Hospitalization_Rate
## 1 13.157895
## 2 12.420382
## 3 NA
## 4 11.981372
## 5 16.685780
## 6 16.044079
## 7 19.398696
## 8 11.088319
## 9 8.825847
## 10 NA
## 11 11.740435
## 12 15.071395
## 13 19.355934
## 14 NA
## 15 4.411765
## 16 8.362369
## 17 9.123867
## 18 14.883402
## 19 NA
## 20 7.680064
## 21 21.032400
## 22 37.236793
## 23 7.468193
## 24 16.056671
## 25 22.367354
## 26 10.252392
## 27 11.802150
## 28 25.396107
## 29 19.677906
## 30 15.683814
## 31 12.910798
## 32 NA
## 33 NA
## 34 14.157973
## 35 9.479243
## 36 13.459399
## 37 22.480886
## 38 6.131479
## 39 8.901515
## 40 NA
## 41 24.642927
## 42 21.947262
## 43 24.349241
## 44 8.255403
## 45 NA
## 46 7.370296
## 47 18.267420
## 48 4.409857
## 49 10.912126
## 50 7.062660
## 51 8.604731
## 52 6.973848
## 53 NA
## 54 16.229976
## 55 4.398777
## 56 10.573248
## 57 28.006668
## 58 16.181230
## 59 NA
## 60 NA
## 61 NA
## 62 NA
## 63 NA
## 64 NA
## 65 NA
## 66 NA
## 67 NA
## 68 NA
## 69 NA
## 70 NA
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## 104 NA
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## 137 NA
## 138 NA
## 139 NA
## 140 NA#covid_ts_confirmedPlot Data
Initial lookinto ploting data points
#install.packages("tidyverse")
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.2.1 ✓ purrr 0.3.3
## ✓ tibble 2.1.3 ✓ dplyr 0.8.4
## ✓ tidyr 1.0.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.4.0## ── Conflicts ─────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(GGally)## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2##
## Attaching package: 'GGally'## The following object is masked from 'package:dplyr':
##
## nasalibrary(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutplot(covid_raw)
byState10 <- covid_raw %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases in First Ten Provinces")
ggplotly(byState10)byState <- covid_raw %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases per Province")
ggplotly(byState)byStateNoNY <- filter(covid_raw, Province_State != 'New York' & Province_State != 'New Jersey' & Province_State != 'Hubei') %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases per Province")
ggplotly(byStateNoNY)#covid_raw %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State) + geom_point(aes(fill=Province_State)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus China)")statesAbove700Deaths <- filter(covid_raw, Deaths > 700) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Province_State)) + geom_point(aes(fill=Province_State)) + ggtitle("Top Mortality States")
ggplotly(statesAbove700Deaths)Map data using Fips codes
library(e1071)
library(usmap)
UScovid_dataset <- filter(covid_raw, Country_Region == 'US' & FIPS != 'NA')
UScovid_dataset## Province_State Country_Region Last_Update Lat
## 1 Alabama US 2020-04-18 22:32:47 32.3182
## 2 Alaska US 2020-04-18 22:32:47 61.3707
## 3 American Samoa US -14.2710
## 4 Arizona US 2020-04-18 22:32:47 33.7298
## 5 Arkansas US 2020-04-18 22:32:47 34.9697
## 6 California US 2020-04-18 22:32:47 36.1162
## 7 Colorado US 2020-04-18 22:32:47 39.0598
## 8 Connecticut US 2020-04-18 22:32:47 41.5978
## 9 Delaware US 2020-04-18 22:32:47 39.3185
## 10 Diamond Princess US 2020-04-18 22:32:47 NA
## 11 District of Columbia US 2020-04-18 22:32:47 38.8974
## 12 Florida US 2020-04-18 22:32:47 27.7663
## 13 Georgia US 2020-04-18 22:32:47 33.0406
## 14 Grand Princess US 2020-04-18 22:32:47 NA
## 15 Guam US 2020-04-18 22:32:47 13.4443
## 16 Hawaii US 2020-04-18 22:32:47 21.0943
## 17 Idaho US 2020-04-18 22:32:47 44.2405
## 18 Illinois US 2020-04-18 22:32:47 40.3495
## 19 Indiana US 2020-04-18 22:32:47 39.8494
## 20 Iowa US 2020-04-18 22:32:47 42.0115
## 21 Kansas US 2020-04-18 22:32:47 38.5266
## 22 Kentucky US 2020-04-18 22:32:47 37.6681
## 23 Louisiana US 2020-04-18 22:32:47 31.1695
## 24 Maine US 2020-04-18 22:32:47 44.6939
## 25 Maryland US 2020-04-18 22:32:47 39.0639
## 26 Massachusetts US 2020-04-18 22:32:47 42.2302
## 27 Michigan US 2020-04-18 22:32:47 43.3266
## 28 Minnesota US 2020-04-18 22:32:47 45.6945
## 29 Mississippi US 2020-04-18 22:32:47 32.7416
## 30 Missouri US 2020-04-18 22:32:47 38.4561
## 31 Montana US 2020-04-18 22:32:47 46.9219
## 32 Nebraska US 2020-04-18 22:32:47 41.1254
## 33 Nevada US 2020-04-18 22:32:47 38.3135
## 34 New Hampshire US 2020-04-18 22:32:47 43.4525
## 35 New Jersey US 2020-04-18 22:32:47 40.2989
## 36 New Mexico US 2020-04-18 22:32:47 34.8405
## 37 New York US 2020-04-18 22:32:47 42.1657
## 38 North Carolina US 2020-04-18 22:32:47 35.6301
## 39 North Dakota US 2020-04-18 22:32:47 47.5289
## 40 Northern Mariana Islands US 2020-04-18 22:32:47 15.0979
## 41 Ohio US 2020-04-18 22:32:47 40.3888
## 42 Oklahoma US 2020-04-18 22:32:47 35.5653
## 43 Oregon US 2020-04-18 22:32:47 44.5720
## 44 Pennsylvania US 2020-04-18 22:32:47 40.5908
## 45 Puerto Rico US 2020-04-18 22:32:47 18.2208
## 46 Rhode Island US 2020-04-18 22:32:47 41.6809
## 47 South Carolina US 2020-04-18 22:32:47 33.8569
## 48 South Dakota US 2020-04-18 22:32:47 44.2998
## 49 Tennessee US 2020-04-18 22:32:47 35.7478
## 50 Texas US 2020-04-18 22:32:47 31.0545
## 51 Utah US 2020-04-18 22:32:47 40.1500
## 52 Vermont US 2020-04-18 22:32:47 44.0459
## 53 Virgin Islands US 2020-04-18 22:32:47 18.3358
## 54 Virginia US 2020-04-18 22:32:47 37.7693
## 55 Washington US 2020-04-18 22:32:47 47.4009
## 56 West Virginia US 2020-04-18 22:32:47 38.4912
## 57 Wisconsin US 2020-04-18 22:32:47 44.2685
## 58 Wyoming US 2020-04-18 22:32:47 42.7560
## Long_ Confirmed Deaths Recovered Active FIPS Incident_Rate
## 1 -86.9023 4712 153 NA 4559 1 100.49272
## 2 -152.4044 314 9 147 305 2 52.53041
## 3 -170.1320 0 0 NA NA 60 0.00000
## 4 -111.4312 4724 180 539 4544 4 64.90155
## 5 -92.3731 1744 38 703 1706 5 67.36121
## 6 -119.6816 30491 1140 NA 29351 6 77.76606
## 7 -105.3111 9047 389 NA 8658 8 159.64882
## 8 -72.7554 17550 1086 NA 16464 9 492.24649
## 9 -75.5071 2538 67 423 2471 10 260.63810
## 10 NA 49 0 0 49 88888 NA
## 11 -77.0268 2666 91 608 2575 11 377.75470
## 12 -81.6868 25492 748 NA 24744 12 120.06063
## 13 -83.6431 17669 673 NA 16996 13 174.26092
## 14 NA 103 0 0 103 99999 NA
## 15 144.7937 136 5 110 131 66 82.81120
## 16 -157.4983 574 9 390 565 15 40.54285
## 17 -114.4788 1655 43 453 1612 16 102.76331
## 18 -88.9861 29160 1259 NA 27901 17 248.47360
## 19 -86.2583 10641 545 NA 10096 18 162.60712
## 20 -93.2105 2513 74 1095 2439 19 95.88994
## 21 -96.7265 1821 85 NA 1736 20 74.67742
## 22 -84.6701 2707 144 979 2563 21 79.07568
## 23 -91.8678 23580 1267 NA 22313 22 512.91354
## 24 -69.3819 847 32 382 815 23 72.14614
## 25 -76.8021 12326 421 771 11905 24 207.39198
## 26 -71.5301 36372 1404 NA 34968 25 529.91271
## 27 -84.5361 30791 2308 3237 28483 26 386.46917
## 28 -93.9002 2209 121 1118 2088 27 44.65833
## 29 -89.6787 3974 152 NA 3822 28 137.26139
## 30 -92.2884 5579 197 NA 5382 29 95.21362
## 31 -110.4544 426 10 234 416 30 49.19242
## 32 -98.2681 1249 24 NA 1225 31 81.88703
## 33 -117.0554 3626 151 NA 3475 32 120.19769
## 34 -71.5639 1342 38 468 1304 33 101.04296
## 35 -74.5210 81420 4070 NA 77350 34 916.66582
## 36 -106.2485 1798 53 382 1745 35 107.82395
## 37 -74.9481 241712 17671 23887 224041 36 1433.55460
## 38 -79.8064 6328 187 NA 6141 37 63.80399
## 39 -99.7840 528 9 183 519 38 87.06866
## 40 145.6739 14 2 9 12 69 25.38807
## 41 -82.7649 10222 451 NA 9771 39 91.48137
## 42 -96.9289 2465 136 1515 2329 40 67.39329
## 43 -122.0709 1844 72 NA 1772 41 46.01862
## 44 -77.2098 31652 1042 NA 30610 42 251.45587
## 45 -66.5901 1118 60 NA 1058 72 38.11267
## 46 -71.5118 4491 137 217 4354 44 423.93481
## 47 -80.9450 4248 119 2633 4129 45 84.28613
## 48 -99.4388 1542 7 552 1535 46 209.54162
## 49 -86.6923 6589 142 3234 6447 47 100.36666
## 50 -97.5635 18704 476 4806 18228 48 81.37636
## 51 -111.8624 2917 25 565 2892 49 101.78367
## 52 -72.7107 803 38 15 765 50 131.51193
## 53 -64.8963 53 3 46 50 78 49.40896
## 54 -78.1700 8053 258 1228 7795 51 101.84350
## 55 -121.4905 11776 613 NA 11163 53 155.98584
## 56 -80.9545 785 16 255 769 54 59.33493
## 57 -89.6165 4199 212 NA 3987 55 81.14682
## 58 -107.3025 309 2 206 307 56 62.14303
## People_Tested People_Hospitalized Mortality_Rate UID ISO3 Testing_Rate
## 1 42538 620 3.2470289 84000001 USA 907.206961
## 2 9655 39 2.8662420 84000002 USA 1615.226458
## 3 3 NA NA 16 ASM 5.391708
## 4 51045 566 3.8103302 84000004 USA 701.291175
## 5 24141 291 2.1788991 84000005 USA 932.435235
## 6 251614 4892 3.7388082 84000006 USA 641.731334
## 7 43307 1755 4.2997679 84000008 USA 764.221442
## 8 55462 1946 6.1880342 84000009 USA 1555.611091
## 9 14017 224 2.6398739 84000010 USA 1439.465825
## 10 NA NA 0.0000000 84088888 USA NA
## 11 13268 313 3.4133533 84000011 USA 1879.988494
## 12 246527 3842 2.9342539 84000012 USA 1161.077449
## 13 74208 3420 3.8089309 84000013 USA 731.878131
## 14 NA NA 0.0000000 84099999 USA NA
## 15 1079 6 3.6764706 316 GUM 657.009420
## 16 22343 48 1.5679443 84000015 USA 1578.133984
## 17 16609 151 2.5981873 84000016 USA 1031.296550
## 18 137404 4340 4.3175583 84000017 USA 1170.825347
## 19 56873 NA 5.1216991 84000018 USA 869.086983
## 20 22947 193 2.9446876 84000019 USA 875.601411
## 21 17676 383 4.6677650 84000020 USA 724.875415
## 22 30596 1008 5.3195419 84000021 USA 893.756702
## 23 137999 1761 5.3731976 84000022 USA 3001.762352
## 24 14923 136 3.7780401 84000023 USA 1271.117865
## 25 65370 2757 3.4155444 84000024 USA 1099.887521
## 26 156806 3729 3.8601122 84000025 USA 2284.545582
## 27 99727 3634 7.4956968 84000026 USA 1251.710281
## 28 44268 561 5.4775917 84000027 USA 894.945583
## 29 38765 782 3.8248616 84000028 USA 1338.937540
## 30 53759 875 3.5310988 84000029 USA 917.474250
## 31 10569 55 2.3474178 84000030 USA 1220.457120
## 32 13677 NA 1.9215372 84000031 USA 896.692524
## 33 30751 NA 4.1643685 84000032 USA 1019.359916
## 34 12852 190 2.8315946 84000033 USA 967.663242
## 35 162536 7718 4.9987718 84000034 USA 1829.909065
## 36 35613 242 2.9477197 84000035 USA 2135.669879
## 37 596532 54339 7.3107665 84000036 USA 3537.934378
## 38 76211 388 2.9551201 84000037 USA 768.420655
## 39 12963 47 1.7045455 84000038 USA 2137.634437
## 40 56 NA 14.2857143 580 MNP 101.552299
## 41 78758 2519 4.4120524 84000039 USA 704.841478
## 42 35536 541 5.5172414 84000040 USA 971.557023
## 43 37583 449 3.9045553 84000041 USA 937.916295
## 44 153965 2613 3.2920511 84000042 USA 1223.158186
## 45 9173 NA 5.3667263 630 PRI 312.707949
## 46 32826 331 3.0505455 84000044 USA 3098.660419
## 47 38833 776 2.8013183 84000045 USA 770.499854
## 48 11660 68 0.4539559 84000046 USA 1584.471634
## 49 90586 719 2.1551070 84000047 USA 1379.847368
## 50 176239 1321 2.5449102 84000048 USA 766.771225
## 51 59944 251 0.8570449 84000049 USA 2091.642294
## 52 12566 56 4.7322540 84000050 USA 2058.006096
## 53 443 NA 5.6603774 850 VIR 412.984301
## 54 51931 1307 3.2037750 84000051 USA 656.753376
## 55 131627 518 5.2055027 84000053 USA 1743.541840
## 56 19440 83 2.0382166 84000054 USA 1469.389976
## 57 48161 1176 5.0488211 84000055 USA 930.724499
## 58 6522 50 0.6472492 84000056 USA 1311.640254
## Hospitalization_Rate
## 1 13.157895
## 2 12.420382
## 3 NA
## 4 11.981372
## 5 16.685780
## 6 16.044079
## 7 19.398696
## 8 11.088319
## 9 8.825847
## 10 NA
## 11 11.740435
## 12 15.071395
## 13 19.355934
## 14 NA
## 15 4.411765
## 16 8.362369
## 17 9.123867
## 18 14.883402
## 19 NA
## 20 7.680064
## 21 21.032400
## 22 37.236793
## 23 7.468193
## 24 16.056671
## 25 22.367354
## 26 10.252392
## 27 11.802150
## 28 25.396107
## 29 19.677906
## 30 15.683814
## 31 12.910798
## 32 NA
## 33 NA
## 34 14.157973
## 35 9.479243
## 36 13.459399
## 37 22.480886
## 38 6.131479
## 39 8.901515
## 40 NA
## 41 24.642927
## 42 21.947262
## 43 24.349241
## 44 8.255403
## 45 NA
## 46 7.370296
## 47 18.267420
## 48 4.409857
## 49 10.912126
## 50 7.062660
## 51 8.604731
## 52 6.973848
## 53 NA
## 54 16.229976
## 55 4.398777
## 56 10.573248
## 57 28.006668
## 58 16.181230#UScovid_dataset$fips <- fips(brew_count_by_state$state)
attach(UScovid_dataset)
UScovid_dataset_fips <- UScovid_dataset[order(FIPS),]
detach(UScovid_dataset)
UScovid_dataset_fips$fips = UScovid_dataset_fips$FIPS
plot_usmap(data = UScovid_dataset_fips, values = "Deaths", color = rgb(.2, .7, 1)) +
labs(title = "Covid Deaths by State", subtitle = "Count of Covid19 Deaths per state") +
scale_fill_continuous(low = "white", high = rgb(.2, .7, 1), name = "Deaths per state", label = scales::comma) + theme(legend.position = "right")
plot_usmap(data = filter(UScovid_dataset_fips, Province_State != 'New York'), values = "Deaths", color = rgb(.2, .7, 1)) +
labs(title = "Covid Deaths by State (New York Removed)", subtitle = "Count of Covid19 Deaths per state") +
scale_fill_continuous(low = "white", high = rgb(.2, .7, 1), name = "Deaths per state", label = scales::comma) + theme(legend.position = "right")
Previous Work - Finding the top Countries by Confirmed Cases
confirmed_by_country <- covid_raw%>% group_by(Country_Region) %>% tally(Confirmed, name = "Confirmed", sort = TRUE)
confirmed_by_country## # A tibble: 8 x 2
## Country_Region Confirmed
## <fct> <int>
## 1 US 732197
## 2 China 83787
## 3 Canada 34356
## 4 Australia 6547
## 5 France 1180
## 6 United Kingdom 1097
## 7 Denmark 195
## 8 Netherlands 177deaths_by_country <- covid_raw%>% group_by(Country_Region) %>% tally(Deaths, name = "Deaths", sort = TRUE)
deaths_by_country## # A tibble: 8 x 2
## Country_Region Deaths
## <fct> <int>
## 1 US 38664
## 2 China 4636
## 3 Canada 1400
## 4 Australia 67
## 5 United Kingdom 34
## 6 France 22
## 7 Netherlands 12
## 8 Denmark 0totals <- merge(confirmed_by_country, deaths_by_country, by="Country_Region")
totals## Country_Region Confirmed Deaths
## 1 Australia 6547 67
## 2 Canada 34356 1400
## 3 China 83787 4636
## 4 Denmark 195 0
## 5 France 1180 22
## 6 Netherlands 177 12
## 7 United Kingdom 1097 34
## 8 US 732197 38664Then reordered by Confirmed
top_to_least <- totals[order(totals$Confirmed, decreasing = TRUE),]
top_to_least## Country_Region Confirmed Deaths
## 8 US 732197 38664
## 3 China 83787 4636
## 2 Canada 34356 1400
## 1 Australia 6547 67
## 5 France 1180 22
## 7 United Kingdom 1097 34
## 4 Denmark 195 0
## 6 Netherlands 177 12top10Confirmed <- top_to_least %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top countries")
ggplotly(top10Confirmed)# At the time, China was the highest and I wanted to look at the rest, now it is much different
top10ConfirmedMinusChina <- subset(top_to_least, Country_Region != "China") %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus China)")
ggplotly(top10ConfirmedMinusChina)# Now removing US instead
top10ConfirmedMinusUS <- subset(top_to_least, Country_Region != "US") %>% head(10) %>% ggplot(aes(x=Deaths, y=Confirmed, fill=Country_Region)) + geom_point(aes(fill=Country_Region)) + ggtitle("Deaths vs Confirmed Cases in Top Countries (Minus US)")
ggplotly(top10ConfirmedMinusUS)